Kubernetes Pod Autoscaling with Custom Metrics

Standard Kubernetes autoscaling often falls short when your applications depend on queue lengths, request rates, or database connections. To build truly responsive infrastructure, you need to scale your workloads based on real-time application behavior. This text-based course guides you through the process of extending the HorizontalPodAutoscaler (HPA) using custom and external metrics. You will start by mastering foundational autoscaling concepts, defining key terminology, and understanding the core architecture of the Kubernetes metrics pipeline. From there, you will explore how to transition from resource-based scaling to application-driven scaling. What you will learn: Understand the core architecture of the Kubernetes metrics server and the HPA controller; Configure custom metrics adapters to expose application-specific data to Kubernetes; Implement scaling policies based on real-world metrics like HTTP request rates and message queue depth; Apply modern observability practices to monitor and troubleshoot scaling decisions; Design robust fallback strategies to prevent scaling failures during metric outages. Through clear explanations and practical configuration examples, you will learn how to design, configure, and refine autoscaling policies that adapt to your specific workload demands. This course is designed for software engineers, DevOps practitioners, and system administrators who are new to advanced Kubernetes scaling and want to move beyond basic CPU and memory metrics. No prior experience with custom metrics adapters is required, though a basic familiarity with Kubernetes concepts is recommended. Start reading today to build self-scaling, resilient Kubernetes deployments.